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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Hypothesis Testing in Econometrics
Organization Unit
Authors
  • Joseph P Romano
  • Azeem M Shaikh
  • Michael Wolf
Language
  • English
Institution University of Zurich
Series Name Working paper series / Institute for Empirical Research in Economics
Number No. 444
ISSN 1424-0459
Date 2009
Abstract Text This paper reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, some of the most important methods are summarized, as well as resampling methodology which is useful to set critical values. Finally, we consider the problem of multiple testing, which has witnessed a burgeoning literature in recent years. Along the way, we incorporate some examples that are current in the econometrics literature. While we include many problems with wellknown successful solutions, we also include open problems that are not easily handled with current technology, stemming from issues like lack of optimality or poor asymptotic approximations.
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